RRH selection and load balancing through Dynamic BBU-RRH Mapping in C-RAN

Cloud radio access network (C-RAN) is a novel architecture that supports the massive growth in traffic of the mobile network. Still, the connection blocking in the C-RAN network may occur due to two main reasons. The first one is not an optimal selection of the radio resource heads (RRHs) to serve the user demands. The second one is not optimal mapping between the baseband units (BBUs) and RRHs. In this paper, we propose a Markov decision process (MDP) based model for RRH selection to serve a connection demand in C-RAN that leads to the user blocking probability minimization as well as the operators' revenue maximization. Moreover, the Ant Colony optimization (ACO) method is used to obtain the best BBU-RRH mapping that leads to further reduction of the user blocking probability and the quality of service (QoS) improvements. The presented simulation results show that the proposed techniques reduce the blocking probability when compared to Received signal strength (RSS) approach.